Interactive image segmentation is a popular field of research in computer vision. The aim of an interactive segmentation framework is to extract one or more foreground objects from the background. Applications for interactive image segmentation include photo editing and medical image analysis. Examples for popular interactive frameworks are Graph Cuts, GrabCut, active contours and interactive level-sets, TV-Seg, efficient belief propagation or Intelligent Scissors.
GrabCut is based on the works of Y. Boykov et al: “Interactive graph cuts for optimal boundary & region segmentation of objects in nd images” Proceedings of the Eighth International Conference On Computer Vision (ICCV-01) (2001), Vol. 1, pp. 105-112 by iteratively performing Graph Cuts. The user provides a rectangle around the desired object as an initial segmentation. This segmentation is used to approximate the appearance of the foreground and background respectively by Gaussian Mixture Models (GMMs). The negative log-likelihoods of the GMMs are assigned to each pixel and Graph Cut is used to update the segmentation. These steps are repeated iteratively. Similarly B. Scheuermann et al: “Interactive image segmentation using level sets and Dempster-Shafer theory of evidence”, Scandinavian Conference on Image Analysis (SCIA) (2011), pp. 656-665 proposed an interactive segmentation scheme based on level-sets and Dempster's theory of evidence. Here the user provides several strokes marking object and background regions. The histograms of these regions are included in a continuous energy function using Dempster's rule of combination. The defined energy function is minimized iteratively by solving the corresponding Euler-Lagrange equation.
In F. Wang et al.: “Efficient label propagation for interactive image segmentation”, International Conference on Machine Learning and Applications (ICMLA) (2007), pp. 136-141 the efficient label propagation (ELP) has been proposed for interactive image segmentation. In an interactive manner, user defined labels are automatically propagated over the remaining unlabeled pixels. ELP is related to the efficient belief propagation proposed by P. F. Felzenszwalb et al.: “Efficient belief propagation for early vision”, Int. J. Comput. Vis. Vol. 70 (2006), pp. 41-54.
In general energy minimization methods based on Graph Cuts or level-sets outperform other methods in terms of segmentation quality and required user effort. However, these energy minimizing methods are hard to implement and hard to control. As an alternative an interactive segmentation scheme based on cellular automata (CA) has been suggested, the so called “GrowCut” algorithm. It is an intuitive method to segment an image and can be implemented very easily. The algorithm has been used in the computer vision community for image denoising and edge detection. Important properties of this approach are the possibility to perform multi-label segmentation tasks and the ability that the algorithm can be extended to higher dimensions.
However, experiments have shown that the algorithm is very sensitive to small changes in the user given seeds. Thus, the algorithm can be caught in local minima, which are not ideal segmentations. That means the user has to invest much time for correcting the seeds to obtain an adequate segmentation. For an interactive segmentation scheme it is not acceptable to pursue a trial-and-error-procedure for the initialization. In addition, compared to other methods, the results of GrowCut are not competitive. One point is that typically given user initializations are strokes, but never hundreds of pixel-like seeds. In experiments the segmentations using GrowCut rarely achieve the quality of energy minimizing segmentation algorithms.